code: def get_gradient(model, Y):
      if isinstance(Y, model.ops.xp.ndarray):
          dY = model.ops.alloc(Y.shape, dtype=Y.dtype)
          dY += model.ops.xp.random.uniform(-1.0, 1.0, Y.shape)
          return dY
      elif isinstance(Y, List):
          return [get_gradient(model, y) for y in Y]
      else:
          raise ValueError(f"Could not get gradient for type {type(Y)}")
  
imports: from typing import List
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  from thinc.api import Adam, Logistic, Ragged, Relu, chain, fix_random_seed, reduce_mean, set_dropout_rate
  from spacy.lang.en import English
  from spacy.lang.en.examples import sentences
  from spacy.ml.extract_spans import _get_span_indices, extract_spans
  from spacy.ml.models import MaxoutWindowEncoder, MultiHashEmbed, build_bow_text_classifier, build_simple_cnn_text_classifier, build_spancat_model, build_Tok2Vec_model
  from spacy.ml.staticvectors import StaticVectors
  from spacy.util import registry
label_desc: Use image node_id 5 for calling a global function with example usage: get_gradient(model, Y) and returns: dY, unknown